Human serum metabolome (better) quantified

An important paper in PLoS One was published last month by Psychogios et al. that details a huge effort that yielded the most comprehensive characterization of the human serum metabolome yet.  I recommend reading the paper if you are casually interested in this area like me, as the intro provides a nice background and context.

Briefly, searchable e-databases for metabolomics has lagged behind other omics areas until recently, but there still wasn’t an attempt to identify the majority of compounds in the blood.  They state that a previous groups’ identification of over 300 metabolites/metabolic features in the plasma with GC-MS and LC-MS was the largest to date, but this effort (with 3 additional techniques) yielded 3564 (confirmed and predicted), plus 665 identified in existing literature!

The project was meant to answer 4 questions as written in the text:

  1. What compounds can be or have ever been identified in blood?
  2. What are the approximate concentration ranges for these metabolites?
  3. What portion of the serum metabolome can be routinely identified and/or quantified using untargeted or “global” metabolomics methods?
  4. What analytical methods (NMR, GC-MS, LC-MS, DFI-MS/MS, etc.) are best suited for comprehensively characterizing the serum metabolome?

They focused this effort on small number of subjects (vs a large heterogeneous population to find differences between genders, races, etc): 21 healthy adults and 9 heart transplant patients for the global metabolite quantification, and 32 healthy males and 37 healthy females for targeted lipidomics.

The following techniques were used to identify and quantify metabolites (%’s in parenthesis are the estimated measurement contribution of the total 4229 metabolites that each technique gives):

  • high-resolution NMR spectroscopy (~1.2%; 49/4229)
  • GC-MS (2.13%; 90/4229)
  • LC-ESI-MS/MS (2.3%; 96/4229)
  • TLC/GC-FID-MS (79.9%; 3381/4229)
  • direct flow injection (DFI) MS/MS (3.3%; 139/4229)
  • Total: 84% (3564/4229)

In addition to these experiments that identified 3564 confirmed and probable metabolites (3,381 were lipids), they sifted through over 2000 books and scientific papers to identify 665 more (16%).

All of this data (including concentrations in healthy and disease contexts when applicable) was deposited in a new database they created called the Serum Metabolome Database (SMDB) which is located here:

Each metabolite is linked to a “MetaboCard” with are linked to other databases: KEGG, PubChem, MetaCyc, ChEBI, PDB, Swiss-Prot, and GenBank, GeneCard, GeneAtlas,  and HGNC IDs for enzymes or proteins that act on the metabolite.  In all, more than 110 data fields are represented for each metabolite.  In addition, linking through the Human Metabolite Database (HMDB) includes nearly 300 hand-drawn metabolic pathway maps (here is one example; I noticed some don’t link properly).  There are a number of different ways to search this database.

There are many important findings in this data, here are a couple:

“One point that is particularly interesting is the fact that the concentration of the average metabolite in normal serum varies by about +/−50%, with some metabolites varying by as much as +/−100% (such as D-glucose, L-lactic acid, L-glutamine, glycine). Therefore, drawing conclusions about potential disease biomarkers without properly taking into account this variation would be ill-advised. We believe that these relatively large ranges of metabolite concentrations are due to a number of factors, including age, gender, genetic background, diurnal variation, health status, activity level, and diet. Indeed, some SMDB entries explicitly show such variations based on the populations (age, gender) from which these metabolite concentrations were derived. Clearly more study on the contributions to the observed variations in serum is warranted, although with thousands of metabolites to measure for dozens of conditions, these studies will obviously require significant technical and human resources.”

In heart transplant patients,

“Interestingly, the cross-sectional variation appears, in general, to be larger than the longitudinal variation.”

The creation of this database was a huge step for future research, as:

“…the majority of published metabolomic studies identify and/or quantify fewer than two dozen metabolites at a time”

Existing studies have used blood metabolites for decades to assess disease risk (e.g. glucose, cholesterol, etc), but now we are able to do it on a much larger scale with metabolic features and fluxes, which should better associate with diseases and disease prediction*, especially on an individual level.

*2 recent examples: Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans and Metabolite profiles and the risk of developing diabetes (amino acid profiles)

There are still limitations in technology sensitivities, bias’ in each technique, expense, sample amount needed, etc.  This group aimed to use techniques that are more commonly available now to find the best ways to obtain the most realistic global snapshots of the metabolome.  Consider this stunning passage:

“…it has been recently postulated that the theoretical number of distinct lipid isoforms in the human body may approach 200,000.”

In sum:

“Experimentally, our data should serve as a useful benchmark from which to compare other technologies and to assess coming methodological improvements in human serum characterization. From a clinical standpoint, we think the information contained in the human serum metabolome database (SMDB) should provide clinicians and clinical chemists a convenient, centralized resource from which to learn more about human serum and its unique biochemical functions.”

Work like this simply amazes me- open databases from different research groups all fluidly connected for the benefit of future research.


Psychogios N, Hau DD, Peng J, Guo AC, Mandal R, Bouatra S, Sinelnikov I, Krishnamurthy R, Eisner R, Gautam B, Young N, Xia J, Knox C, Dong E, Huang P, Hollander Z, Pedersen TL, Smith SR, Bamforth F, Greiner R, McManus B, Newman JW, Goodfriend T, & Wishart DS (2011). The human serum metabolome. PloS one, 6 (2) PMID: 21359215